Communication and Sensing: From Compressed Sampling to Model-based Deep Learning

ABSTRACT: The famous Shannon-Nyquist theorem has become a landmark in analog to digital conversion and the development of digital signal processing algorithms. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power.  In this talk we consider a general framework for communication and sensing including sub-Nyquist sampling, quantization and processing in space, time and frequency which allows to dramatically reduce the number of antennas, sampling rates, number of bits and band occupancy in a variety of applications.  Our framework relies on exploiting signal structure, quantization and the processing task in both standard processing and in deep learning networks leading to a new framework for model-based deep learning. It also allows for the development of efficient joint radar-communication systems. We consider applications of these ideas to a variety of problems in wireless communications, imaging, efficient massive MIMO systems, automotive radar and ultrasound imaging and show several demos of real-time sub-Nyquist prototypes including a wireless ultrasound probe, sub-Nyquist automotive radar, cognitive radio and radar, dual radar-communication systems, analog precoding, sparse antenna arrays, and a deep Viterbi decoder. We end by discussing more generally how models can be used in deep learning methods with application to a variety of communication settings.

Related Papers:

1. Monga, Y. Li, and Y. C. Eldar, “Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing”, IEEE Signal Processing Magazine, vol. 38, issue 2, pp. 18-44, March 2021.
2. Shlezinger, J. Whang, Y. C. Eldar and A. G. Dimakis, “Model-Based Deep Learning“, Submitted to IEEE Transactions on Signal Processing, December 2020.
3. Shlezinger, Y. C. Eldar and M. R. D. Rodrigues, “Hardware-Limited Task-Based Quantization”, IEEE Transactions on Signal Processing, vol. 67, issue 20, pp. 5223-5238, October 2019.
4. Ma, N. Shlezinger, T. Huang, Y. Liu, and Y. C. Eldar, “Joint Radar-Communications Strategies for Autonomous Vehicles“,IEEE Signal Processing Magazine, vol. 37, issue 4, pp. 85-97, July 2020.
5. J. G. van Sloun, R. Cohen and Y. C. Eldar, “Deep Learning in Ultrasound Imaging“, Proceedings of the IEEE, vol. 108, issue 1, pp. 11-29, January 2020.

BIO: Yonina Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel, where the heads the center for biomedical engineering. She was previously a Professor in the Department of Electrical Engineering at the Technion, where she held the Edwards Chair in Engineering. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering both from Tel-Aviv University (TAU), Tel-Aviv, Israel, in 1995 and 1996, respectively, and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT), Cambridge, in 2002. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), the Award for Women with Distinguished Contributions, the Andre and Bella Meyer Lectureship, the Career Development Chair at the Technion, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion’s Award for Excellence in Teaching (two times). She received several best paper awards and best demo awards together with her research students and colleagues, was selected as one of the 50 most influential women in Israel, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing and a member of several IEEE Technical Committees and Award Committees.

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